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[CVPR 2024 Highlight] This is the official PyTorch implementation of "TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models".
[ECCV24] MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization
[CVPR 2024] DeepCache: Accelerating Diffusion Models for Free
arXiv LaTeX Cleaner: Easily clean the LaTeX code of your paper to submit to arXiv
🔮 ChatGPT Desktop Application (Mac, Windows and Linux)
[ICLR 2024 Spotlight] This is the official PyTorch implementation of "EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models"
[ICCV 2023] Q-Diffusion: Quantizing Diffusion Models.
Implementation of Post-training Quantization on Diffusion Models (CVPR 2023)
Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference
A Compressed Stable Diffusion for Efficient Text-to-Image Generation [ECCV'24]
Official implementation of the paper "The Stable Signature Rooting Watermarks in Latent Diffusion Models"
Adversarial Robustness, White-box, Adversarial Attack
Code for "On Adaptive Attacks to Adversarial Example Defenses"
Google Research
PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)
Codebase for reproducible benchmarking experiments in MedMNIST v2
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
A new adversarial purification method that uses the forward and reverse processes of diffusion models to remove adversarial perturbations.
❄️🔥 Visual Prompt Tuning [ECCV 2022] https://arxiv.org/abs/2203.12119
[pip install medmnist] 18x Standardized Datasets for 2D and 3D Biomedical Image Classification
Generative Models by Stability AI
[ICLR 2022] Understanding and Improving Graph Injection Attack by Promoting Unnoticeability
CAIRI Supervised, Semi- and Self-Supervised Visual Representation Learning Toolbox and Benchmark
LaTeX 양식 : R&E, 졸업논문, beamer 등등 - 컴파일된 결과 pdf파일 미포함
Official Implementation for PlugIn Inversion
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them [NeurIPS 2020]
Denoising Diffusion Probabilistic Models